# encode the labels, converting them from strings to integers
le = LabelEncoder()
labels = le.fit_transform(labels)
# scale the input image pixels to the range [0, 1], then transform
# the labels into vectors in the range [0, num_classes] -- this
# generates a vector for each label where the index of the label
# is set to `1` and all other entries to `0`
data = np.array(data) / 255.0
labels = np_utils.to_categorical(labels, 2)
# partition the data into training and testing splits, using 75%
# of the data for training and the remaining 25% for testing
print("[INFO] constructing training/testing split...")
(trainData, testData, trainLabels, testLabels) = train_test_split(
data, labels, test_size=0.25, random_state=42)